code: def test_to_dense_batch_jit():
      @torch.jit.script
      def to_dense_batch_jit(
          x: Tensor,
          batch: Tensor,
          fill_value: Tensor,
      ) -> Tuple[Tensor, Tensor]:
          return to_dense_batch(x, batch, fill_value=fill_value)
  
      x = torch.randn(6, 2)
      batch = torch.tensor([0, 0, 1, 2, 2, 2])
  
      out, mask = to_dense_batch_jit(
          x, batch, fill_value=torch.tensor(0.0)
      )
      assert out.size() == (3, 3, 2)
      assert mask.size() == (3, 3)
  
file_path: pytorch_geometric/test/utils/test_to_dense_batch.py
imports: from typing import Tuple
  import pytest
  import torch
  from torch import Tensor
  from torch_geometric.experimental import set_experimental_mode
  from torch_geometric.testing import onlyFullTest
  from torch_geometric.utils import to_dense_batch
label_desc: Use image node_id 3 for calling a global function with example usage: test_to_dense_batch_jit() and returns: to_dense_batch